A Novel Gesture-based CAPTCHA Design for Smart Devices

CAPTCHAs have been widely used in Web applications to prevent service abuse. With the evolution of computing environment from desktop computing to ubiquitous computing, more and more users are accessing Web applications on smart devices where touch based interactions are dominant. However, the majority of CAPTCHAs are designed for use on computers and laptops which do not reflect the shift of interaction style very well. In this paper, we propose a novel CAPTCHA design to utilise the convenience of touch interface while retaining the needed security. This is achieved through using a hybrid challenge to take advantages of human’s cognitive abilities. A prototype is also developed and found to be more user friendly than conventional CAPTCHAs in the preliminary user acceptance test

TAPCHA: An Invisible CAPTCHA Scheme

TAPCHA is a universal CAPTCHA scheme designed for touch-enabled smart devices such as smartphones, tablets and smartwatches. The main difference between TAPCHA and other CAPTCHA schemes is that TAPCHA retains its security by making the CAPTCHA test ‘invisible’ for the bot. It then utilises context effects to maintain the readability of the instruction for human users which eventually guarantees the usability of the scheme. Two reference designs, namely TAPCHA SHAPE & SHADE and TAPCHA MULTI are developed to demonstrate the use of this scheme

CAPTCHA: Attacks and Weaknesses against OCR technology

The basic challenge in designing these obfuscating CAPTCHAs is to make them easy enough that users are not dissuaded from attempting a solution, yet still too difficult to solve using available computer vision algorithms. As Modern technology grows this gap however becomes thinner and thinner. It is possible to enhance the security of an existing text CAPTCHA by system-apically adding noise and distortion, and arranging characters more tightly. These measures, however, would also make the characters harder for humans to recognize, resulting in a higher error rates and higher Network load .This paper presents few of most active attacks on text CAPTCHAs existing today

An Accessible Web CAPTCHA Design for Visually Impaired Users

In the realm of computing, CAPTCHAs are used to determine if a user engaging with a system is a person or a bot. The most common CAPTCHAs are visual in nature, requiring users to recognize images comprising distorted characters or objects. For people with visual impairments, audio CAPTCHAs are accessible alternatives to standard visual CAPTCHAs. Users are required to enter or say the words in an audio-clip when using Audio CAPTCHAs. However, this approach is time-consuming and vulnerable to machine learning algorithms, since automated speech recognition (ASR) systems could eventually understand the content of audio with the improvement of the technique. While adding background noise may deceive ASR systems temporarily, it may cause people to have difficulties de- ciphering the information, thus reducing usability. To address this, we designed a more secure and accessible web CAPTCHA based on the capabilities of people with visually impairments, obviating the need for sight via the use of audio and movement, while also using object detection techniques to enhance the accessibility of the CAPTCHA

Learning to Associate Words and Images Using a Large-scale Graph

We develop an approach for unsupervised learning of associations between co-occurring perceptual events using a large graph. We applied this approach to successfully solve the image captcha of China's railroad system. The approach is based on the principle of suspicious coincidence. In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images. They must quickly select the relevant images in order to purchase their train tickets. This problem presents several challenges: (1) the teaching labels for both the Chinese phrases and the images were not available for supervised learning, (2) no pre-trained deep convolutional neural networks are available for recognizing these Chinese phrases or the presented images, and (3) each captcha must be solved within a few seconds. We collected 2.6 million captchas, with 2.6 million deformed Chinese phrases and over 21 million images. From these data, we constructed an association graph, composed of over 6 million vertices, and linked these vertices based on co-occurrence information and feature similarity between pairs of images. We then trained a deep convolutional neural network to learn a projection of the Chinese phrases onto a 230-dimensional latent space. Using label propagation, we computed the likelihood of each of the eight images conditioned on the latent space projection of the deformed phrase for each captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on average. Our work, in answering this practical challenge, illustrates the power of this class of unsupervised association learning techniques, which may be related to the brain's general strategy for associating language stimuli with visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201

SECURITY AND USER EXPERIENCE: A HOLISTIC MODEL FOR CAPTCHA USABILITY ISSUES

CAPTCHA is a widely adopted security measure in the Web, and is designed to effectively distinguish humans and bots by exploiting human’s ability to recognize patterns that an automated bot is incapable of. To counter this, bots are being designed to recognize patterns in CAPTCHAs. As a result, CAPTCHAs are now being designed to maximize the difficulty for bots to pass human interaction proof tests, while making it quite an arduous task even for humans as well. The approachability of CAPTCHA is increasingly being questioned because of the inconvenience it causes to legitimate users. Irrespective of the popularity, CAPTCHA is indispensable if one wants to avoid potential security threats. We investigated the usability issues associated with CAPTCHA. We built a holistic model by identifying the important concepts associated with CAPTCHAs and its usability. This model can be used as a guide for the design and evaluation of CAPTCHAs